The Adversity-Thresholded All-Option Functional Degradation Theory (AT-AOFDT): A Domain-General, Time-Optional, Medium-Independent Framework

AbstractThis manuscript presents Version 1.1 of the Adversity-Thresholded All-Option Functional Degradation Theory (AT-AOFDT), a proposed analytical framework for examining systems in which adversity and constraints may eliminate every functionally acceptable option while leaving one or more actions physically, institutionally, computationally, or procedurally available. Its defining distinction is between the absence of feasible actions and the non-emptiness of the feasible action set combined with an empty set of functionally acceptable policies.The framework separates domain, medium, process, temporal structure, system state, resources, constraints, decision policies, outcomes, and evaluation parameters. It permits discrete process-indexed, continuous-time, and event-order representations without requiring all systems to use the same variables, units, or performance thresholds. Its central condition is formalized through a non-empty feasible policy set and an empty acceptable policy subset. A domain-specific evaluation architecture makes the criteria, reference states, tolerances, and assessment horizons explicit.The proposed architecture includes twelve mechanisms, twenty analytical layers, fourteen relevant research domains, seven testable hypotheses, a sixteen-step research roadmap, and a sovereign-debt case study designed to illustrate the framework rather than establish its empirical validity. Cross-domain generalization is treated as a structural and empirical question, not as a consequence of notation alone. The manuscript distinguishes definition-level results from substantive causal hypotheses and identifies counterexamples, alternative explanations, uncertainty analysis, and independent validation as necessary components of subsequent research.AT-AOFDT is presented as a research proposal. Its originality, explanatory advantage, predictive performance, and empirical generality remain to be established through systematic comparison with existing work in decision theory, constrained optimization, viability theory, robust control, reliability engineering, and related fields.